Human activity recognition (HAR) remains an essential field of research with increasing real-
world applications ranging from healthcare to industrial environments. As the volume of …

Human Activity Recognition (HAR) based on inertial data is an increasingly diffused task on
embedded devices, from smartphones to ultra low-power sensors. Due to the high …

In this work, a new custom design of an anomaly detection and classification system is
proposed. It is composed of a convolutional Auto-Encoder (AE) hardware design to perform …

Personalised health and fitness provide users with information regarding their wellbeing and
an opportunity to inform healthcare services for better patient outcomes. Underpinning this …

This paper proposes a novel low-power HW accelerator for audio PDM-to-PCM conversion
based on artificial neural network. The system processes samples from a digital MEMS …

Binary neural networks (BNNs) have attracted significant interest for the implementation of
deep neural networks (DNNs) on resource-constrained edge devices, and various BNN …

The accuracy and computational complexity of keyword spotting (KWS) systems are heavily
influenced by the choice of audio features in speech signals. This letter introduces a novel …

In view of the excellent portability and privacy protection of wearable sensor devices, human
activity recognition (HAR) of wearable devices has increased applications in human …

This brief proposes a new approach based on a tiny neural network to convert Pulse Density
Modulation (PDM) signals acquired from digital Micro-Electro-Mechanical System (MEMS) …

MEMS pressure sensors are widely used in several application fields, such as industrial,
medical, automotive, etc, where they are required to be increasingly accurate and reliable …